ER rule classifier with an optimization operator recommendation

2021 ◽  
pp. 1-13
Author(s):  
Xiaoyan Wang ◽  
Jianbin Sun ◽  
Qingsong Zhao ◽  
Yaqian You ◽  
Jiang Jiang
Keyword(s):  
Classification Accuracy ◽  
Comprehensive Analysis ◽  
Small Sample ◽  
Empirical Knowledge ◽  
Classification Methods ◽  
Turbofan Engine ◽  
Training Samples ◽  
Optimization Operator ◽  
Recommendation Strategy

It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation.

scholarly journals Multiscale Information Fusion for Hyperspectral Image Classification Based on Hybrid 2D-3D CNN

2021 ◽  
Vol 13 (12) ◽  
pp. 2268
Author(s):  
Hang Gong ◽  
Qiuxia Li ◽  
Chunlai Li ◽  
Haishan Dai ◽  
Zhiping He ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Small Sample ◽  
Hyperspectral Images ◽  
Sample Problem ◽  
Training Samples ◽  
Small Sample Problem ◽  
3D Cnn ◽  
Hyperspectral Classification

Hyperspectral images are widely used for classification due to its rich spectral information along with spatial information. To process the high dimensionality and high nonlinearity of hyperspectral images, deep learning methods based on convolutional neural network (CNN) are widely used in hyperspectral classification applications. However, most CNN structures are stacked vertically in addition to using a onefold size of convolutional kernels or pooling layers, which cannot fully mine the multiscale information on the hyperspectral images. When such networks meet the practical challenge of a limited labeled hyperspectral image dataset—i.e., “small sample problem”—the classification accuracy and generalization ability would be limited. In this paper, to tackle the small sample problem, we apply the semantic segmentation function to the pixel-level hyperspectral classification due to their comparability. A lightweight, multiscale squeeze-and-excitation pyramid pooling network (MSPN) is proposed. It consists of a multiscale 3D CNN module, a squeezing and excitation module, and a pyramid pooling module with 2D CNN. Such a hybrid 2D-3D-CNN MSPN framework can learn and fuse deeper hierarchical spatial–spectral features with fewer training samples. The proposed MSPN was tested on three publicly available hyperspectral classification datasets: Indian Pine, Salinas, and Pavia University. Using 5%, 0.5%, and 0.5% training samples of the three datasets, the classification accuracies of the MSPN were 96.09%, 97%, and 96.56%, respectively. In addition, we also selected the latest dataset with higher spatial resolution, named WHU-Hi-LongKou, as the challenge object. Using only 0.1% of the training samples, we could achieve a 97.31% classification accuracy, which is far superior to the state-of-the-art hyperspectral classification methods.

An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

2019 ◽  
Vol 21 (9) ◽  
pp. 631-645 ◽  
Author(s):  
Saeed Ahmed ◽  
Muhammad Kabir ◽  
Zakir Ali ◽  
Muhammad Arif ◽  
Farman Ali ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Early Stage ◽  
Small Sample Size ◽  
Feature Selection Method ◽  
Small Sample ◽  
Expression Data ◽  
Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

scholarly journals Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

2021 ◽  
Author(s):  
Chenguang Li ◽  
Hongjun Yang ◽  
Long Cheng
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Support Vector ◽  
Classification Methods ◽  
Experimental Paradigm ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Method ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

scholarly journals A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging

2021 ◽  
pp. 102007
Author(s):  
Debesh Jha ◽  
Sharib Ali ◽  
Steven Hicks ◽  
Vajira Thambawita ◽  
Hanna Borgli ◽  
...  
Keyword(s):  
Gastrointestinal Endoscopy ◽  
Comprehensive Analysis ◽  
Classification Methods

Temporal dynamics of encrusting communities during the Late Devonian: a case study from the Central Devonian Field, Russia

Paleobiology ◽  
2017 ◽  
Vol 43 (4) ◽  
pp. 550-568 ◽  
Author(s):  
Michał Zatoń ◽  
Tomasz Borszcz ◽  
Michał Rakociński
Keyword(s):  
Temporal Dynamics ◽  
Small Sample ◽  
Time Interval ◽  
Faunal Turnover ◽  
Central Devonian Field ◽  
Before And After ◽  
Substrate Size ◽  
Mass Extinction Event ◽  
Diversity Dynamics

AbstractIn this study we focused on the dynamics of encrusting assemblages preserved on brachiopod hosts collected from upper Frasnian and lower Famennian deposits of the Central Devonian Field, Russia. Because the encrusted brachiopods come from deposits bracketing the Frasnian/Famennian (F/F) boundary, the results also shed some light on ecological differences in encrusting communities before and after the Frasnian–Famennian (F-F) event. To explore the diversity dynamics of encrusting assemblages, we analyzed more than 1300 brachiopod valves (substrates) from two localities. Taxon accumulation plots and shareholder quorum subsampling (SQS) routines indicated that a reasonably small sample of brachiopod host valves (n=50) is sufficient to capture the majority of the encrusting genera recorded at a given site. The richness of encrusters per substrate declined simultaneously with the number of encrusting taxa in the lower Famennian, accompanied by a decrease in epibiont abundance, with a comparable decrease in mean encrustation intensity (percentage of bioclasts encrusted by one or more epibionts). Epibiont abundance and occupancy roughly mirror each other. Strikingly, few ecological characteristics are correlated with substrate size, possibly reflecting random settlement of larvae. Evenness, which is negatively correlated with substrate size, shows greater within-stage variability among samples than between Frasnian and Famennian intervals and may indicate the instability of early Famennian biocenoses following the faunal turnover. The occurrence distribution of encrusters points to nonrandom associations and exclusions among several encrusting taxa. However, abundance and occupancy of microconchids remained relatively stable throughout the sampled time interval. The notable decline in abundance (~60%) and relatively minor decline in diversity (~30%) suggest jointly that encrusting communities experienced ecological collapse rather than a major mass extinction event. The differences between the upper Frasnian and lower Famennian encrusting assemblages may thus record a turnover associated with the F-F event.

scholarly journals Gastric Cancer Microbiome

Pathobiology ◽  
2021 ◽  
Vol 88 (2) ◽  
pp. 156-169
Author(s):  
Williams Fernandes Barra ◽  
Dionison Pereira Sarquis ◽  
André Salim Khayat ◽  
Bruna Cláudia Meireles Khayat ◽  
Samia Demachki ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Small Sample Size ◽  
Total Sample ◽  
Clinical Situation ◽  
Gastric Carcinogenesis ◽  
Comprehensive Analysis ◽  
Small Sample ◽  
Metagenomic Data ◽  
Clinical Scenarios ◽  
H Pylori

Identifying a microbiome pattern in gastric cancer (GC) is hugely debatable due to the variation resulting from the diversity of the studied populations, clinical scenarios, and metagenomic approach. <i>H. pylori</i> remains the main microorganism impacting gastric carcinogenesis and seems necessary for the initial steps of the process. Nevertheless, an additional non-<i>H. pylori</i> microbiome pattern is also described, mainly at the final steps of the carcinogenesis. Unfortunately, most of the presented results are not reproducible, and there are no consensual candidates to share the <i>H. pylori</i> protagonists. Limitations to reach a consistent interpretation of metagenomic data include contamination along every step of the process, which might cause relevant misinterpretations. In addition, the functional consequences of an altered microbiome might be addressed. Aiming to minimize methodological bias and limitations due to small sample size and the lack of standardization of bioinformatics assessment and interpretation, we carried out a comprehensive analysis of the publicly available metagenomic data from various conditions relevant to gastric carcinogenesis. Mainly, instead of just analyzing the results of each available publication, a new approach was launched, allowing the comprehensive analysis of the total sample amount, aiming to produce a reliable interpretation due to using a significant number of samples, from different origins, in a standard protocol. Among the main results, <i>Helicobacter</i> and <i>Prevotella</i> figured in the “top 6” genera of every group. <i>Helicobacter</i> was the first one in chronic gastritis (CG), gastric cancer (GC), and adjacent (ADJ) groups, while <i>Prevotella</i> was the leader among healthy control (HC) samples. Groups of bacteria are differently abundant in each clinical situation, and bacterial metabolic pathways also diverge along the carcinogenesis cascade. This information may support future microbiome interventions aiming to face the carcinogenesis process and/or reduce GC risk.

scholarly journals Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Wenning Wang ◽  
Xuebin Liu ◽  
Xuanqin Mou
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Classification Problem ◽  
Classification Performance ◽  
Spectral Structure ◽  
Limited Sample ◽  
Sample Classification ◽  
Training Samples ◽  
Traditional Classification ◽  
Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Exploring trainers’ engaging instructional practices: a collective case study

2016 ◽  
Vol 40 (2) ◽  
pp. 111-127 ◽  
Author(s):  
Vishal Arghode ◽  
Jia Wang
Keyword(s):  
Learning Styles ◽  
Learning Outcomes ◽  
Training Session ◽  
Research Area ◽  
Small Sample ◽  
Training Context ◽  
Content Type ◽  
Collective Case Study ◽  
Study Approach

Purpose – This study aims to explore the phenomenon of training engagement from the trainers’ perspective. Specifically, two questions guided this inquiry. First, how do trainers define engagement in the training context? and What strategies do trainers use to engage trainees? Design/methodology/approach – The collective case study approach was adopted for this qualitative study. Seven cases were selected for in-depth analyses. Data were collected through individual, face-to-face interviews and analyzed using the constant comparative analysis method. Findings – Major findings suggest that engaging training practices take various forms. They include being trainee-centered, maximizing learning through entertaining and interesting instruction, accommodating different learning styles, eliciting trainee participation by creating an encouraging learning environment and connecting with trainees by building rapport early in a training session. Research limitations/implications – The small sample limits the generalizability of the findings. However, this study expands training literature by focusing on an under-explored research area, the role of engaging trainees in maximizing learning outcomes. Practical implications – For trainers, this study offered some specific strategies they can use to engage learners in the training context to achieve desired learning outcomes. In addition, the seven cases selected for this study may be used as a benchmark against which both experienced and novice trainers compared their own practices. Originality/value – This is one of very few qualitative studies with a focus on emotional aspects involved in training. The rich data from this study shed light on areas for future improvement, particularly regarding how to effectively engage trainees to maximize learning outcomes.

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